Project Summary Advances in genome mining, and consequent realization of untapped microbial diversity has lead to renewed interest in natural product (NP) discovery. NPs are a rich source of therapeutic candidates, with a long history of successes in combating infectious diseases, and oncology. The emergence of antibiotic-resistant bacteria like C. difficile and carbapenem-resistant enterobacteiaceae (CRE) only emphasizes the desperate, and growing need for new therapeutics. Investigation of microenvironments for novel NP compounds is labor-intensive, and requires numerous molecular biology, and biochemical techniques to isolate and characterize novel NP compounds. In addition, months of effort to isolate and identify a compound can be wasted if the compound was previously discovered. High-throughput methods are needed to quickly identify drug leads from a sample, with minimal upfront investment. Tandem mass spectrometry (MS) is a high-throughput technology capable of sensitively identifying molecules. Unlike nuclear magnetic resonance spectroscopy, which is a widely used in NP research for structure elucidation and dereplication, tandem MS can be used to analyze small amounts of compounds directly from the environmental extract. While MS offers the hope of rapid NP identification, 98% of spectra cannot be matched to existing spectral libraries. Quorum is a computational platform that organizes tandem MS data into molecular networks. Molecular networks connect related compounds that differ by a mutation, modification, or adduct. Quorum aims to increase NP compound annotations compared to previous approaches by integrating spectral library search and structure database search. In addition, annotations of known compounds can be automatically propagated to related, but unknown compounds via similarity of their tandem mass spectra. Quorum will be deployed as a web application to enable interactive visual interrogation of the network topology and underlying data analysis. Given the design of a MS experiment (e.g. case versus control or time-series), the application will perform statistical analysis to highlight significantly enriched compounds and sub-networks. These features can be used to quickly identify novel drug leads or to optimize growth and extraction protocols.